Summary
Jorge Gonzalez is an AI/ML engineer with a decade of international experience building production-ready GenAI, RAG and ML systems across finance, manufacturing and public-sector clients. He has led teams and delivered end-to-end solutions—from designing Neo4j knowledge graphs and Agentic RAG chatbots to training and deploying LLMs on Azure, Databricks and OpenAI stacks. Jorge combines hands-on model development (PyTorch/TensorFlow, prompt engineering, embeddings, LLM fine-tuning) with pragmatic MLOps using Azure DevOps, Airflow and Docker to ensure repeatable, monitored pipelines. His background spans revenue management forecasting, semantic search, defect detection and supply-chain SaaS for NGOs, reflecting a strong mix of research and product focus. Fluent in English and proficient in French, he brings a collaborative, critical-thinking leadership style that emphasizes measurable evaluation (RAGAS) and data-driven decision making. Based in Monterrey, he pairs formal training in Big Data and mathematics with a proven record of turning complex AI ideas into deployed business impact.
10 years of coding experience
6 years of employment as a software developer
Ingeniería, Ingeniería at Tecnológico de Monterrey
Master's degree, Big Data Solutions, Master's degree, Big Data Solutions at Barcelona Technology School
Engineer's degree, Matemáticas e informática, Engineer's degree, Matemáticas e informática at Monash University
English, French, Spanish